library(tidyverse) # Colección de paquetes para ciencia de datos (incluye ggplot2, dplyr, tidyr, readr, purrr, tibble)
library(reshape) # Herramientas para reorganizar datos
library(Hmisc) # resúmenes estadísticos
library(limma) # Análisis de datos de expresión genética
library(AnnotationDbi) # Interfaz para bases de datos de anotaciones bioinformáticas
library(org.Hs.eg.db) # Datos de anotación para genes humanos
library(VennDiagram) # Generación de diagramas de Venn
library(gridExtra) # Mostrar varias gráficas
library(patchwork) # Combinar múltiples ggplots en un único plot
library(ggrepel) # Mejora la visualización de texto en ggplots evitando solapamientos de texto
library(Rtsne) # Implementación de t-SNE
library(umap) # Implementación de UMAP
library(ggVennDiagram) # diagrama de Venn
library(fastDummies) # Para realizar el one-hot encodingDescargar los datos. Escalo los datos de la matriz para tener homogeneidad en las representaciones.
# covariables
ROSMAP_RINPMIAGESEX_covs <- readRDS("~/Library/CloudStorage/OneDrive-UNIVERSIDADDEMURCIA/Documentos/Fernando/Master Bioinformatica/TFM/datos/ROSMAP_RINPMIAGESEX_covs.rds")
covs <- ROSMAP_RINPMIAGESEX_covs
rownames(covs) <- covs$mrna_id
covs$study <- as.factor(covs$study)
covs$projid <- as.character(covs$projid)
covs$ceradsc <- as.factor(covs$ceradsc)
covs$cogdx <- as.factor(covs$cogdx)
covs$neuroStatus <- as.factor(covs$neuroStatus)
#datos corregidos
ROSMAP_RINPMIAGESEX_resids <- readRDS("~/Library/CloudStorage/OneDrive-UNIVERSIDADDEMURCIA/Documentos/Fernando/Master Bioinformatica/TFM/datos/ROSMAP_RINPMIAGESEX_resids.rds")
mat_exp <- scale(ROSMAP_RINPMIAGESEX_resids)One-hot encoding de la matriz de covariables
covs2 <- data.frame(matrix(ncol = 0, nrow = nrow(covs))) # Hago un dataframe vacio para meter los datos procesados
for (colname in names(covs)) {
if (is.factor(covs[[colname]]) & length(levels(covs[[colname]])) > 2) {
dummy_df <- dummy_cols(covs[colname],
remove_selected_columns = TRUE) # quitar las variables iniciales
dummy_df <- data.frame(lapply(dummy_df, factor))
covs2 <- cbind(covs2, dummy_df)
} else {
covs2[[colname]] <- covs[[colname]] # si son numéricas o de otra clase, las añadimos igual
}
}
rownames(covs2) <- covs2$mrna_id
covs2Filtrar la matriz Mnxm a Mnxm’, con m’ << n para filtrar los predictores, en este caso de la matriz de expresión mat_exp.
# geneset de Alzheimer extraidos de KEGG
tab <- getGeneKEGGLinks(species="hsa")
tab$Symbol <- mapIds(org.Hs.eg.db, tab$GeneID,
column="SYMBOL", keytype="ENTREZID")
paths <- getKEGGPathwayNames(species="hsa")
geneset_alz <- tab$Symbol[tab$PathwayID=="hsa05010"]Visualizamos si se han seleccionado todos los genes y cuantos del geneset de KEGG. Nuestra matriz tiene 305 genes de un total 384 del geneset.
venn.plot <- venn.diagram(
x = list(GenesMatriz = colnames(mat_exp), GenesetAlzheimer = geneset_alz),
category.names = c("Matrix Genes", "Geneset Alzheimer"),
filename = NULL,
output = FALSE, # Asegura que no se exporte a un archivo
fill = c("#440154ff", '#fde725ff'),
cex = 1, # Aumenta el tamaño del texto
fontface = "bold",
cat.cex = 1, # Aumenta el tamaño del texto de las categorías
cat.fontface = "bold",
cat.default.pos = "text",
cat.pos = 25, #posicion de las categoricas
cat.dist = 0.1, #distancia de las categoricas
rotation.degree = 0,
margin = 0.1, # hacerla más pequeña
lwd=0.5,
lty = "dashed", # Estilo de línea discontinua
edge.col = "grey", # Color de los bordes
main = "Alzheimer genes in exp matrix",
main.fontface= "bold",
main.cex = 2,
main.pos = c(0.5, 1)
)
grid.newpage() # Asegura que el lienzo esté limpio
grid.draw(venn.plot)# Scree plot con los datos escalados
var_exp <- pca_KEGG$sdev^2
prop_var_exp <- var_exp / sum(var_exp)
cum_var_exp <- cumsum(prop_var_exp)
df_var_exp <- data.frame(Comp = 1:length(prop_var_exp), VarExp = prop_var_exp)
df_cum_var_exp <- data.frame(Comp = 1:length(cum_var_exp), CumVarExp = cum_var_exp)
ggplot(df_var_exp[1:20,], aes(x = Comp, y = VarExp)) +
geom_bar(stat = "identity", fill = "skyblue") +
geom_line(aes(group = 1), color = "blue") +
geom_point(color = "blue") +
theme_minimal() +
labs(x = "Principal components", y = "Variance", title = "Scree Plot scaled") +
ylim(c(0,1)) +
geom_line(data = df_cum_var_exp[1:20,], aes(x = Comp, y = CumVarExp), color="#8B1A1A") +
geom_point(data = df_cum_var_exp[1:20,], aes(x = Comp, y = CumVarExp), color = "red") +
geom_bar(data = df_cum_var_exp[1:20,], aes(x = Comp, y = CumVarExp), stat = "identity", fill = "red", alpha= 0.25) +
annotate("text", x = 4, y = 0.85, label = "Cumulative Scree Plot", color = "#8B1A1A", size = 4) +
geom_text(data = df_cum_var_exp[seq(0,20,2),], aes(x=Comp, y = CumVarExp +0.04, label = round(CumVarExp, 2)))Selecciono los outliers de las dos primeras PC con el doble de SD.
outlierspc1 <- as.data.frame(pca_KEGG$x[abs(pca_KEGG$x[,1]) > 2*(pca_KEGG$sdev[1]),1])
outlierspc2 <- as.data.frame(pca_KEGG$x[abs(pca_KEGG$x[,2]) > 2*(pca_KEGG$sdev[2]),2])df <- as.data.frame(pca_KEGG$x)
plot1 <- ggplot(df, aes(x = PC1)) +
geom_density(fill = "#00CED1", alpha = 0.5) +
geom_vline(xintercept = mean(df$PC1) + 2*pca_KEGG$sdev[1], linetype = "dashed", color = "blue") +
geom_vline(xintercept = mean(df$PC1) - 2*pca_KEGG$sdev[1], linetype = "dashed", color = "blue") +
labs(title = "PC1 density with 2*SD scaled PCA") +
geom_text(data = outlierspc1,
aes(x = outlierspc1[,1], y= 0, label = rownames(outlierspc1)),
vjust = 1.5, hjust = 0, size = 3, color = "#E9965A", angle = 90, fontface= "bold") +
geom_rug(data = as.data.frame(pca_KEGG$x), aes(x= PC1, y = 0), color= ifelse(abs(pca_KEGG$x[,1]) > 2*(pca_KEGG$sdev[1]), "#8B1A1A", "grey")) +
theme_minimal()
plot2 <- ggplot(df, aes(x = PC2)) +
geom_density(fill = "#00CED1", alpha = 0.5) +
geom_vline(xintercept = mean(df$PC2) + 2*pca_KEGG$sdev[2], linetype = "dashed", color = "blue") +
geom_vline(xintercept = mean(df$PC2) - 2*pca_KEGG$sdev[2], linetype = "dashed", color = "blue") +
labs(title = "PC2 density with 2*SD scaled PCA") +
geom_text(data = outlierspc2,
aes(x = outlierspc2[,1], y= 0, label = rownames(outlierspc2)),
vjust = 1.5, hjust = 0, size = 3, color = "#E9965A", angle = 90, fontface= "bold") +
geom_rug(data = as.data.frame(pca_KEGG$x), aes(x= PC2, y = 0), color= ifelse(abs(pca_KEGG$x[,2]) > 2*(pca_KEGG$sdev[2]), "#8B1A1A", "grey")) +
theme_minimal()
grid.arrange(plot1, plot2, ncol = 1)mPC1.pos <- rownames(outlierspc1[outlierspc1[, 1] > 0 , , drop = F])
mPC1.neg <- rownames(outlierspc1[outlierspc1[, 1] < 0 , , drop = F ])
mPC2.pos <- rownames(outlierspc2[outlierspc2[, 1] > 0 , , drop = F ])
mPC2.neg <- rownames(outlierspc2[outlierspc2[, 1] < 0 , , drop = F])# Asegurarse de que todos son vectores del mismo largo para el dataframe final
max_length <- max(length(mPC1.pos),
length(mPC1.neg),
length(mPC2.pos),
length(mPC2.neg))
# Normalizar la longitud de los vectores (en caso de que alguno sea más corto)
rownames_mPC1.pos <- c(mPC1.pos, rep("", max_length - length(mPC1.pos)))
rownames_mPC1.neg <- c(mPC1.neg, rep("", max_length - length(mPC1.neg)))
rownames_mPC2.pos <- c(mPC2.pos, rep("", max_length - length(mPC2.pos)))
rownames_mPC2.neg <- c(mPC2.neg, rep("", max_length - length(mPC2.neg)))
final_table <- data.frame(
mPC1.pos = rownames_mPC1.pos,
mPC1.neg = rownames_mPC1.neg,
mPC2.pos = rownames_mPC2.pos,
mPC2.neg = rownames_mPC2.neg
)
final_tableset.seed(1234)
tsne <- Rtsne(mat_exp_alz_genes, dims = 2, theta = 0.0)
tsne.data <- as.data.frame(tsne$Y)
row.names(tsne.data) <- row.names(mat_exp_alz_genes)
tsne.data.covs <- merge(tsne.data, covs, by = "row.names")
tsne.data.covs$Row.names <- NULL
row.names(tsne.data.covs) <- tsne.data.covs$mrna_idrownames(tsne.data) <- rownames(mat_exp_alz_genes)
# Calcular la media y la desviación estándar para cada componente de t-SNE
mean.tsne1 <- mean(tsne.data[,1])
sd.tsne1 <- sd(tsne.data[,1])
mean.tsne2 <- mean(tsne.data[,2])
sd.tsne2 <- sd(tsne.data[,2])
# Identificar muestras a más de 2 desviaciones estándar de la media
outliers.tsne1 <- tsne.data[abs(tsne.data[,1] - mean.tsne1) > 2 * sd.tsne1, ]
outliers.tsne2 <-tsne.data[abs(tsne.data[,2] - mean.tsne2) > 2 * sd.tsne2, ]mtSNE1.pos <- rownames(outliers.tsne1[outliers.tsne1[, 1] > 0, , drop = F])
mtSNE1.neg <- rownames(outliers.tsne1[outliers.tsne1[, 1] < 0, , drop = F])
mtSNE2.pos <- rownames(outliers.tsne2[outliers.tsne2[, 1] > 0, , drop = F])
mtSNE2.neg <- rownames(outliers.tsne2[outliers.tsne2[, 1] < 0, , drop = F])# Asegurarse de que todos son vectores del mismo largo para el dataframe final
max_length <- max(length(mtSNE1.pos),
length(mtSNE1.neg),
length(mtSNE2.pos),
length(mtSNE2.neg))
# Normalizar la longitud de los vectores (en caso de que alguno sea más corto)
rownames_mtSNE1.pos <- c(mtSNE1.pos, rep("", max_length - length(mtSNE1.pos)))
rownames_mtSNE1.neg <- c(mtSNE1.neg, rep("", max_length - length(mtSNE1.neg)))
rownames_mtSNE2.pos <- c(mtSNE2.pos, rep("", max_length - length(mtSNE2.pos)))
rownames_mtSNE2.neg <- c(mtSNE2.neg, rep("", max_length - length(mtSNE2.neg)))
final_table <- data.frame(
mtSNE1.pos = rownames_mtSNE1.pos,
mtSNE1.neg = rownames_mtSNE1.neg,
mtSNE2.pos = rownames_mtSNE2.pos,
mtSNE2.neg = rownames_mtSNE2.neg
)
final_tablelocal.config <- umap.defaults
# local.config$n_neighbors <- 4
# local.config$n_components <- 2
# local.config$n_epochs <- 100
# local.config$metric<- "euclidean"
set.seed(1234)
umap.ad <- umap(mat_exp_alz_genes,random_stage=1234, local.config)
umap.data <- as.data.frame(umap.ad$layout)
row.names(umap.data) <- row.names(mat_exp_alz_genes)
umap.data.covs <- merge(umap.data, covs, by = "row.names")
umap.data.covs$Row.names <- NULL
row.names(umap.data.covs) <- umap.data.covs$mrna_idrownames(umap.data) <- rownames(mat_exp_alz_genes)
# Calcular la media y la desviación estándar para cada componente de t-SNE
mean.umap1 <- mean(umap.data[,1])
sd.umap1 <- sd(umap.data[,1])
mean.umap2 <- mean(umap.data[,2])
sd.umap2 <- sd(umap.data[,2])
# Identificar muestras a más de 2 desviaciones estándar de la media
outliers.umap1 <- umap.data[abs(umap.data[,1] - mean.umap1) > 2 * sd.umap1, ]
outliers.umap2 <-umap.data[abs(umap.data[,2] - mean.umap2) > 2 * sd.umap2, ]mUMAP1.pos <- rownames(outliers.umap1[outliers.umap1[,1] > 0 , , drop = F])
mUMAP1.neg <- rownames(outliers.umap1[outliers.umap1[,1] < 0 , , drop = F])
mUMAP2.pos <- rownames(outliers.umap2[outliers.umap2[,1] > 0 , , drop = F])
mUMAP2.neg <- rownames(outliers.umap2[outliers.umap2[,1] < 0 , , drop = F])# Asegurarse de que todos son vectores del mismo largo para el dataframe final
max_length <- max(length(mUMAP1.pos),
length(mUMAP1.neg),
length(mUMAP2.pos),
length(mUMAP2.neg))
# Normalizar la longitud de los vectores (en caso de que alguno sea más corto)
rownames_mtSNE1.pos <- c(mUMAP1.pos, rep("", max_length - length(mUMAP1.pos)))
rownames_mtSNE1.neg <- c(mUMAP1.neg, rep("", max_length - length(mUMAP1.neg)))
rownames_mtSNE2.pos <- c(mUMAP2.pos, rep("", max_length - length(mUMAP2.pos)))
rownames_mtSNE2.neg <- c(mUMAP2.neg, rep("", max_length - length(mUMAP2.neg)))
final_table <- data.frame(
mUMAP1.pos = rownames_mtSNE1.pos,
mUMAP1.neg = rownames_mtSNE1.neg,
mUMAP2.pos = rownames_mtSNE2.pos,
mUMAP2.neg = rownames_mtSNE2.neg
)
final_table# PCA
for (i in rownames(covs2)){
if (i %in% mPC1.pos){
covs2[i, "sampleset_PCA"] <- "mPC1 positive"
}
else if (i %in% mPC1.neg){
covs2[i, "sampleset_PCA"] <- "mPC1 negative"
}
else if (i %in% mPC2.pos ){
covs2[i, "sampleset_PCA"] <- "mPC2 positive"
}
else if (i %in% mPC2.neg){
covs2[i, "sampleset_PCA"] <- "mPC2 negative"
}
else {
covs2[i, "sampleset_PCA"] <- "Not in both"
}
}
covs2$sampleset_PCA <- as.factor(covs2$sampleset_PCA)
data.frame(table(covs2$sampleset_PCA))#tsne
for (i in rownames(covs2)){
if (i %in% mtSNE1.pos){
covs2[i, "sampleset_tSNE"] <- "mtSNE1 positive"
}
else if (i %in% mtSNE1.neg){
covs2[i, "sampleset_tSNE"] <- "mtSNE1 negative"
}
else if (i %in% mtSNE2.pos){
covs2[i, "sampleset_tSNE"] <- "mtSNE2 positive"
}
else if (i %in% mtSNE2.neg){
covs2[i, "sampleset_tSNE"] <- "mtSNE2 negative"
} else {
covs2[i, "sampleset_tSNE"] <- "Not in both"
}
}
covs2$sampleset_tSNE <- as.factor(covs2$sampleset_tSNE)
data.frame(table(covs2$sampleset_tSNE))# UMAP
for (i in rownames(covs2)){
if (i %in% mUMAP1.pos){
covs2[i, "sampleset_UMAP"] <- "mUMAP1 positive"
}
else if (i %in% mUMAP1.neg){
covs2[i, "sampleset_UMAP"] <- "mUMAP1 negative"
}
else if (i %in% mUMAP2.pos){
covs2[i, "sampleset_UMAP"] <- "mUMAP2 positive"
}
else if (i %in% mUMAP2.neg){
covs2[i, "sampleset_UMAP"] <- "mUMAP2 negative"
} else {
covs2[i, "sampleset_UMAP"] <- "Not in both"
}
}
covs2$sampleset_UMAP <- as.factor(covs2$sampleset_UMAP)
data.frame(table(covs2$sampleset_UMAP))Voy a comparar en una tabla los ratios de las covariables de los outliers comparando con los ratios de covariables de los no outliers
calculo.ratios <- function(covs2){
ratios <- data.frame()
ratios$ratios <- numeric(0)
for (i in names(covs2)) {
if (class(covs2[[i]]) == "factor") {
if (grepl("sampleset_", i) | grepl("batch", i)){
next
} else {
frecuencia <- table(covs2[[i]])
if (length(frecuencia) == 2) {
ratio <- frecuencia[2] / frecuencia[1]
nuevafila <- data.frame(ratios = ratio)
row.names(nuevafila) <- i
ratios <- rbind(ratios, nuevafila)
}
}
}
}
return(ratios)
}data.frame.ratios <- function(df, sampleset) {
niveles.sampleset <- levels(df[[sampleset]])
ratios <- data.frame(matrix(ncol = 0, nrow = length(rownames(calculo.ratios(covs2)))))
for (i in niveles.sampleset) {
ratio <- df[df[[sampleset]] == i, ] %>%
calculo.ratios()
nombre.filas <- rownames(ratio)
colnames(ratio)[1] <- i
ratios <- cbind(ratios, ratio)
rownames(ratios) <- nombre.filas
}
return(ratios)
}ratios.PCA.kegg <- data.frame.ratios(covs2, "sampleset_PCA")
ratios.tsne.kegg <- data.frame.ratios(covs2, "sampleset_tSNE")
ratios.umap.kegg <- data.frame.ratios(covs2, "sampleset_UMAP")ratio.ouliers.vs.no.ouliers <- data.frame(matrix(ncol = 0, nrow = length(ratios.PCA.kegg)))
for (i in colnames(ratios.PCA.kegg)) {
if (i == "Not in both") {
next
} else {
ratio <- data.frame(log2(ratios.PCA.kegg[[i]] / ratios.PCA.kegg[["Not in both"]]))
nombre.filas <- rownames(ratios.PCA.kegg)
colnames(ratio)[1] <- paste(i, " vs No outliers")
ratio.ouliers.vs.no.ouliers <- cbind(ratio.ouliers.vs.no.ouliers, ratio)
rownames(ratio.ouliers.vs.no.ouliers) <- nombre.filas
}
}library(factoextra)
p1 <- fviz_cos2(pca_KEGG, choice = "var", axes = 1, top= 20, fill = "#00AFBB", ggtheme = theme_minimal()) + labs(y = "Cos2", title = "Dim 1")
p2 <- fviz_cos2(pca_KEGG, choice = "var", axes = 20, top= 20, fill = "#00AFBB", ggtheme = theme_minimal()) + labs(y = "Cos2", title = "Dim 2")
grid.arrange(p1, p2, ncol=1)pca_rotation <- round(data.frame(pca_KEGG$rotation[,1:2]),2)
pca_rotation <- pca_rotation[order(-pca_rotation[,1]),]
reactable(pca_rotation)fviz_pca_biplot(pca_KEGG,
geom.ind = "none",
geom_var = c("arrow", "text"),
col.var = "cos2",
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = F,
)pca.data.kegg <- data.frame(pca_KEGG$x)[,1:2]
colnames(pca.data.kegg) <- c("PC1.KEGG", "PC2.KEGG")
covs2 <- merge(covs2, pca.data.kegg, by="row.names")
rownames(covs2) <- covs2$mrna_id
covs2$Row.names <- NULLpca.data.kegg <- data.frame(pca_KEGG$x)[,1:2]
colnames(pca.data.kegg) <- c("PC1.KEGG", "PC2.KEGG")
covs2_df <- covs2[, c(1, 40), drop = FALSE]
para.plot <- merge(pca.data.kegg, covs2_df, by = "row.names")
rownames(para.plot) <- para.plot$Row.names
para.plot$Row.names <- NULL
ggplot(para.plot, aes_string(x = "PC1.KEGG", y = "PC2.KEGG", color = "sampleset_PCA")) +
geom_point(show.legend = TRUE) +
geom_text_repel(aes(label=ifelse(mrna_id %in% rownames(outlierspc1) | mrna_id %in% rownames(outlierspc2), as.character(gsub("_.*", "", covs2$mrna_id)), "")),
color = "black",
max.overlaps = 10, # Reduce el número máximo de solapamientos
point.padding = unit(0.2, "lines"), # Menos padding alrededor de los puntos
size = 3,
fontface = "bold",
segment.size = 0.2, # Líneas de guía más finas
segment.color = 'grey50',
max.segment.length = unit(0.5, "lines"), # Líneas de guía más cortas
arrow = arrow(length = unit(0.02, "npc"), type = "closed", ends = "last")) +
theme_minimal() +
labs(title = names(covs2)[i],
x = "PC1.KEGG", y = "PC2.KEGG") +
theme(
legend.title = element_blank(),
legend.text = element_text(size = 12),
text = element_text(size = 12),
legend.key.size = unit(0.5, "cm"),
plot.margin = margin(5, 5, 5, 5)# Agrega un margen alrededor de la gráfica si es necesario
)